





"""simple docstring"""





import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			flatten_dict(SCREAMING_SNAKE_CASE     )
      return flax_params


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{}

      lowerCAmelCase    		=			{
          """token_embedder""": """embeddings""",
          """encoder_norm""": """layernorm""",
          """kernel""": """weight""",
          """.out""": """.output""",
          """scale""": """weight""",
          """embedders_0.pos_embedding""": """row_embedder.weight""",
          """embedders_1.pos_embedding""": """column_embedder.weight""",
      }

      lowerCAmelCase    		=			{
          """query""": """attention.query""",
          """key""": """attention.key""",
          """value""": """attention.value""",
          """output.dense""": """output""",
          """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
          """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
          """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
          """mlp.""": """mlp.DenseReluDense.""",
          """pre_mlp_layer_norm""": """mlp.layer_norm""",
          """self_attention.o""": """self_attention.attention.o""",
          """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
          """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
          """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
          """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
      }

      for key in flax_dict.keys():
            if "target" in key:
                  # remove the first prefix from the key
                  lowerCAmelCase    		=			""".""".join(key[1:]     )

                  # rename the key
                  for old, new in CONVERSION_MAPPING.items():
                        lowerCAmelCase    		=			new_key.replace(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

                  if "decoder" in new_key:
                        for old, new in DECODER_CONVERSION_MAPPING.items():
                              lowerCAmelCase    		=			new_key.replace(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

                  if "layers" in new_key and "decoder" not in new_key:
                        # use regex to replace the layer number
                        lowerCAmelCase    		=			re.sub(R"""layers_(\d+)"""				,		R"""layer.\1"""				,		SCREAMING_SNAKE_CASE     )
                        lowerCAmelCase    		=			new_key.replace("""encoder"""				,		"""encoder.encoder"""     )

                  elif "layers" in new_key and "decoder" in new_key:
                        # use regex to replace the layer number
                        lowerCAmelCase    		=			re.sub(R"""layers_(\d+)"""				,		R"""layer.\1"""				,		SCREAMING_SNAKE_CASE     )

                  lowerCAmelCase    		=			flax_dict[key]

      lowerCAmelCase    		=			{}
      # convert converted_dict into torch format
      for key in converted_dict.keys():
            if ("embed_tokens" not in key) and ("embedder" not in key):
                  lowerCAmelCase    		=			torch.from_numpy(converted_dict[key].T     )
            else:
                  lowerCAmelCase    		=			torch.from_numpy(converted_dict[key]     )

      return converted_torch_dict


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[str]=False				,		SCREAMING_SNAKE_CASE	:		Any=False     ):







      '''simple docstring'''




      lowerCAmelCase    		=			get_flax_param(SCREAMING_SNAKE_CASE     )

      if not use_large:
            lowerCAmelCase    		=			PixaStructVisionConfig()
            lowerCAmelCase    		=			PixaStructTextConfig()
      else:
            lowerCAmelCase    		=			PixaStructVisionConfig(
                hidden_size=15_36				,		d_ff=39_68				,		num_attention_heads=24				,		num_hidden_layers=18     )
            lowerCAmelCase    		=			PixaStructTextConfig(hidden_size=15_36				,		d_ff=39_68				,		num_heads=24				,		num_layers=18     )
      lowerCAmelCase    		=			PixaStructConfig(
          vision_config=encoder_config.to_dict()				,		text_config=decoder_config.to_dict()				,		is_vqa=SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			rename_and_convert_flax_params(SCREAMING_SNAKE_CASE     )
      model.load_state_dict(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""     )
      lowerCAmelCase    		=			PixaStructImageProcessor()
      lowerCAmelCase    		=			PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE				,		tokenizer=SCREAMING_SNAKE_CASE     )

      if use_large:
            lowerCAmelCase    		=			40_96

      lowerCAmelCase    		=			True

      # mkdir if needed
      os.makedirs(SCREAMING_SNAKE_CASE				,		exist_ok=SCREAMING_SNAKE_CASE     )

      model.save_pretrained(SCREAMING_SNAKE_CASE     )
      processor.save_pretrained(SCREAMING_SNAKE_CASE     )

      print("""Model saved in {}""".format(SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--use_large", action="store_true", help="Use large model.")
       parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()

       convert_pixastruct_original_pytorch_checkpoint_to_hf(
           args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
       )













"""simple docstring"""





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      lowerCAmelCase    		=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      i -= len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			i // 3
      lowerCAmelCase    		=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      lowerCAmelCase    		=			(
          chars_incl
          + random(SCREAMING_SNAKE_CASE				,		quotient + remainder     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      )
      lowerCAmelCase    		=			list(SCREAMING_SNAKE_CASE     )
      shuffle(SCREAMING_SNAKE_CASE     )
      return "".join(SCREAMING_SNAKE_CASE     )

      # random is a generalised function for letters, characters and numbers


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) < min_length:
            # Your Password must be at least 8 characters long
            return False

      lowerCAmelCase    		=			any(char in ascii_uppercase for char in password     )
      lowerCAmelCase    		=			any(char in ascii_lowercase for char in password     )
      lowerCAmelCase    		=			any(char in digits for char in password     )
      lowerCAmelCase    		=			any(char in punctuation for char in password     )

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			int(input("""Please indicate the max length of your password: """     ).strip()     )
      lowerCAmelCase    		=			input(
          """Please indicate the characters that must be in your password: """     ).strip()
      print("""Password generated:"""				,		password_generator(SCREAMING_SNAKE_CASE     )     )
      print(
          """Alternative Password generated:"""				,		alternative_password_generator(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		)
      print("""[If you are thinking of using this passsword, You better save it.]"""     )


if __name__ == "__main__":
       main()






